Neural networks and virtual machines for advanced processing and execution

ABSTRACT

Provided are exemplary systems and methods for secure intelligent networked architecture, processing and execution. Exemplary embodiments include an intelligent networked architecture comprising an intelligent agent, a secure cloud of a plurality of specialized intelligent historical agents, a plurality of secure cloud based specialized insight servers configured to transform secure digital data into a scrubbed situational deployment trigger, and an intelligent operational agent configured to receive the scrubbed situational deployment trigger.

CROSS REFERENCE TO RELATED APPLICATION

This continuation application claims the priority benefit of U.S. patentapplication Ser. No. 17/872,985 filed Jul. 25, 2022, titled “AdvancedSecure Intelligent Networked Architecture, Processing and Execution,” asissued on Sep. 26, 2023 as U.S. Pat. No. 11,768,952, which is acontinuation application that claims the priority benefit of U.S. patentapplication Ser. No. 17/086,190 filed Oct. 30, 2020, titled “SecureIntelligent Networked Architecture, Processing and Execution,” as issuedon Aug. 2, 2022 as U.S. Pat. No. 11,403,416, which is a continuation ofU.S. patent application Ser. No. 16/517,418 filed Jul. 19, 2019, titled“Secure Intelligent Networked Architecture, Processing and Execution,”as issued on Nov. 3, 2020 as U.S. Pat. No. 10,824,753, which is acontinuation of U.S. patent application Ser. No. 15/201,005, filed Jul.1, 2016, titled “Secure Intelligent Networked Architecture, Processingand Execution,” as issued on Sep. 24, 2019 as U.S. Pat. No. 10,423,800.The aforementioned disclosures are hereby incorporated by referenceherein in their entireties including all references cited therein.

FIELD OF THE TECHNOLOGY

The embodiments disclosed herein are related to secure intelligentnetworked architecture, processing and execution.

SUMMARY

Provided herein are exemplary systems and methods for secure intelligentnetworked architecture, processing and execution.

Exemplary embodiments include an intelligent networked architecturecomprising an intelligent agent having a specialized hardware processor,the intelligent agent configured to automatically determine a first, asecond, a third, a fourth, a fifth, a sixth, a seventh and an eighthdigital data element; a secure cloud of a plurality of specializedintelligent historical agents, each historical agent having aspecialized hardware processor and a memory further comprising securedigital data corresponding to the first, the second, the third and thefourth digital data elements; a plurality of secure cloud basedspecialized insight servers, each insight server having a specializedhardware processor, the plurality of secure cloud based specializedinsight servers configured to receive from the secure cloud of theplurality of specialized historical agents the secure digital datacorresponding to the first, the second, the third and the fourth digitaldata elements and configured to transform the secure digital datacorresponding to the first, the second, the third and the fourth digitaldata elements as directed by the fifth, the sixth, the seventh and theeighth digital data elements into a scrubbed situational deploymenttrigger; and an intelligent operational agent having a specializedhardware processor, the intelligent operational agent configured toreceive the scrubbed situational deployment trigger.

Further exemplary embodiments include the intelligent operational agentconfigured to determine a tenth digital element, and configured todeploy the scrubbed situational deployment trigger based on the tenthdigital element. The intelligent networked architecture may furthercomprise a load balancer having a specialized hardware processor, theload balancer configured to distribute the secure digital data receivedby the plurality of secure cloud based specialized insight servers. Theload balancer may be further configured to distribute processing of thesecure digital data by the plurality of secure cloud based specializedinsight servers.

Some exemplary embodiments include the first digital data element, thesecond digital data element, and the third digital data element randomlygenerated by a hardware based random number generator.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an exemplary system for secure intelligentnetworked architecture, processing and execution.

FIGS. 2A-2B represent a flowchart of an exemplary method for intelligentnetworked architecture, processing and execution exemplary method forintelligent networked architecture, processing and execution.

FIG. 3 is a table of exemplary digital data elements for secureintelligent networked architecture, processing and execution.

FIGS. 4A-4B represent a flowchart of an exemplary method for intelligentnetworked architecture, processing and execution.

FIGS. 5A-5B represent a flowchart of an exemplary method for intelligentnetworked architecture, processing and execution.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

FIG. 1 is a diagram of an exemplary system for secure intelligentnetworked architecture, processing and execution.

The exemplary system 100 as shown in FIG. 1 includes a secure cloudbased specialized intelligent historical agent 101, activity server 102,secure intelligent agent 103, optional hardware based random numbergenerator machine 103A, secure intelligent operational agent 104, securecloud based specialized insight servers and/or virtual insight machines105, optional data transfer corridors 106A-106D, and scrubbedsituational deployment trigger 107.

According to various exemplary embodiments, the secure cloud basedspecialized intelligent historical agent 101 is a non-generic computingdevice comprising non-generic computing components. It may comprisespecialized dedicated processors configured to seek, copy and transmitmassive amounts of secure digital data from the secure cloud basedspecialized intelligent historical agent 101 to the secure cloud basedspecialized insight servers and/or virtual insight machines 105.

In some embodiments, the secure cloud based specialized intelligenthistorical agent 101 is situated behind a firewall (not shown). In someembodiments, the secure cloud based specialized intelligent historicalagent 101 encrypts the secure digital data before storing it anddecrypts the secure digital data before copying and transmitting it tothe secure cloud based specialized insight servers and/or virtualinsight machines 105. The decrypting may be performed as a separate stepin advance of copying and transmitting to increase the speed in whichthe specialized dedicated processors may copy and transmit the securedigital data. According to further embodiments, the specializeddedicated processors may copy as a separate step the secure digital databefore transmitting it to increase the speed in which the specializeddedicated processors retrieve and transmit the secure digital data. Ineven further embodiments, the secure cloud based specialized intelligenthistorical agent 101 may, without human involvement, automaticallysearch, retrieve, and encrypt new data to update stored secure digitaldata.

The activity server 102, according to exemplary embodiments, is a thirdparty server with activity influenced by numerous external agents,factors and conditions. The activity server 102 receives secureinstructions from the secure intelligent operational agent 104 and theactivity server 102 transmits digital data as influenced by the numerousexternal agents, factors and conditions to the secure intelligentoperational agent 104. In some exemplary embodiments, the activityserver 102 is associated with one or more sports teams, companies,markets, exchanges, firms or associations. In further exemplaryembodiments, one way or two way communication between the activityserver 102 and the secure intelligent operational agent 104 may be overa dedicated secure wired channel or over a dedicated secured wirelesschannel, with or without encryption/decryption and with or without afirewall separating the two.

The secure intelligent agent 103, according to some exemplaryembodiments (although not limited to), is a non-generic computing devicecomprising non-generic computing components. It may comprise specializeddedicated hardware processors to determine and transmit digital dataelements to the secure cloud based specialized intelligent historicalagent 101. In further exemplary embodiments, the secure intelligentagent 103 comprises a specialized device having circuitry, loadbalancing, and specialized hardware processors, and artificialintelligence, including machine learning. Numerous determination stepsby the secure intelligent agent 103 as described herein may be made byan automatic machine determination without human involvement, includingbeing based on a previous outcome or feedback (e.g. an automaticfeedback loop) provided by the secure intelligent networkedarchitecture, processing and/or execution as described herein.

The optional hardware based random number generator machine 103A,according to various exemplary embodiments, may determine and/ortransmit one or more digital data elements to the secure intelligentagent 103, to the secure cloud based specialized intelligent historicalagent 101 and/or to the secure cloud based specialized insight serversand/or virtual insight machines 105.

The secure intelligent operational agent 104, according to variousexemplary embodiments, is a non-generic computing device comprisingnon-generic computing components. It may comprise specialized dedicatedhardware processors to determine and transmit one or more digital dataelements to the secure cloud based specialized intelligent historicalagent 101. The secure intelligent operational agent 104 may comprisespecialized dedicated hardware processors to determine and transmit newdata to update secure digital data in the secure cloud based specializedintelligent historical agent 101. The secure intelligent operationalagent 104 may comprise specialized dedicated hardware processors todetermine, request and receive secure digital data from the secure cloudbased specialized intelligent historical agent 101. The secureintelligent operational agent 104 may comprise specialized dedicatedhardware processors to determine and transmit secure instructions to theactivity server 102. The secure intelligent operational agent 104, maycomprise specialized dedicated hardware processors to receive from theactivity server 102 digital data as influenced by the numerous externalagents, factors and conditions. The secure intelligent operational agent104 may comprise specialized dedicated hardware processors to receivetransformed data, files, and/or visually perceptible elements from thesecure cloud based specialized insight servers and/or virtual insightmachines 105. The secure intelligent operational agent 104 may comprisespecialized dedicated hardware processors to determine and transmitfeedback information to the secure intelligent agent 103. Additionally,the functions of the specialized dedicated hardware processors of thesecure intelligent operational agent 104 may be distributed among manyhardware processors or integrated or consolidated into fewer hardwareprocessors.

The secure intelligent operational agent 104, in some exemplaryembodiments, receives massive amounts of secure digital data from thesecure cloud based specialized intelligent historical agent 101 in anextremely short period of time. Such secure digital data may be receivedby the secure intelligent operational agent 104 as triggered by thesecure intelligent operational agent 104 determining a digital dataelement, determining a list based upon the determined digital dataelement and requesting members of the list (comprising secure digitaldata) from the secure cloud based specialized intelligent historicalagent 101. Accordingly, bandwidth and processing between the secureintelligent operational agent 104 and the secure cloud based specializedintelligent historical agent 101 maybe optimized through dedicatedhardware, including processors, wired and/or wireless channels,circuits, switches, firewalls, data compressors, data scrubbers and thelike.

The data transmissions to and from the secure intelligent operationalagent 104, according to various exemplary embodiments, are highlyvaluable and/or sensitive. Accordingly, numerous measures may be takenincluding use of hardware based encryption and/or decryption of thetransmissions, use of a dedicated wired and/or wireless channels fortransmissions, use of specialized hardware circuitry and/or switches fortransmissions.

In a further exemplary embodiment, the data transmissions to and fromthe secure intelligent operational agent 104 may comprise actual or truetransmissions and decoy or false transmissions to deter or throw-off anyoutside agency intercepting, recording, observing or otherwiseinterfering with such transmissions.

The secure cloud based specialized insight servers and/or virtualinsight machines 105, according to various exemplary embodiments,receive secure digital data from the secure cloud based specializedintelligent historical agent 101. The secure cloud based specializedinsight servers and/or virtual insight machines 105, according tovarious exemplary embodiments, receive at least one digital data elementfrom the secure intelligent agent 103. The secure cloud basedspecialized insight servers and/or virtual insight machines 105,according to various exemplary embodiments, utilize massivesophisticated computing resources as described herein to producetransformed digital data, files, scrubbed files and/or visuallyperceptible digital data elements. One such transformation productproduced is the scrubbed situational deployment trigger 107 that istransmitted to the secure intelligent operational agent 104.

In certain exemplary embodiments, the secure cloud based specializedinsight servers and/or virtual insight machines 105 further comprise amaster virtual machine server including a single secure cloud resourceresponsible for generating all of or most of the herein describedvirtual machines.

According to various exemplary embodiments, a virtual machine maycomprise an emulation of a particular computer system. Virtual machinesoperate based on the computer architecture and functions of a real orhypothetical computer, and their implementations may involve specializedhardware, software, or a combination of both.

In further exemplary embodiments, a CloudManager is configured to acluster of cloud computing instances for processing large amounts ofdata. The CloudManager serves as the user interface to handle theordering and cancelling of virtual computing instances. Additionally,the CloudManager may allow for detailed customization of the virtualmachines. For example, Random Access Memory (“RAM”), processor speed,number of processors, network details, security/encryption, and/ormemory may be detailed for each virtual machine and/or all virtualmachines. Once the cluster of cloud computing instances is ordered andrunning, the CloudManager is “listening” for idle machines and“assigning” any idle machine processing responsibilities.

A cloud-based computing environment is a resource that typicallycombines the computational power of a large grouping of processorsand/or that combines the storage capacity of a large grouping ofcomputer memories or storage devices.

For example, 150 8-core virtual machines may be utilized to processhundreds of billions of calculations in three to four hours.

Additionally, each virtual machine may transform historical data andperformance data into a neural network usable data set. In someexemplary embodiments, a neural network usable data set comprises anaggregation of data points organized into one or more sets.

For example, for a particular time period, such as each day (or eachminute, hour, month, year(s), decades, etc.), for any particular item,the historical data and performance data are grouped together as a dataset.

According to further embodiments, each virtual machine may create aneural network base. The neural network base, along with the neuralnetwork usable data set forms a neural network. Creating a neuralnetwork base, according to some exemplary embodiments, includesprocessing a layer of input data and then processing that dataalgorithmically to compare the output of algorithms against knownoutput.

A neural network base may comprise a set of algorithms used toapproximate against data inputs. These algorithms are able to storetested values within itself and store an error rate.

A neural network may comprise a neural network base and its underlyingalgorithms in tandem with a neural network usable data set. The neuralnetwork usable data set may function as a data feeder for the neuralnetwork base to calculate and/or otherwise interact with.

In various exemplary embodiments, feeding of a data point may beperformed by the neural network iteratively requesting to process thenext data point in the usable data set.

Data points, according to many exemplary embodiments, may include butare not limited to performance data and historical data that the neuronetwork has access to as part of its useable data set.

Also shown in FIG. 1 are optional data transfer corridors 106A-106D.According to further exemplary embodiments, one or more optional datatransfer corridors may be installed at certain locations in theintelligent networked architecture. The optional data transfer corridorsare hardware-based secure, high speed data transfer corridors, eachhaving specialized processors and switches. The optional data transfercorridors facilitate the unilateral or bilateral transfer of massiveamounts of data especially in those situations where the extremely quicktransfer of massive amounts of data is required.

FIGS. 2A-2B represent a flowchart of an exemplary method 200 forintelligent networked architecture, processing and execution.

At step 201, a secure intelligent agent determines a first digital dataelement. In some exemplary embodiments, the first digital data elementmay include a resource. In further exemplary embodiments, other factorsmay represent the first digital data element.

At step 202, the secure intelligent agent determines a second digitaldata element. In some exemplary embodiments, the second digital dataelement is a segment for the resource. In further exemplary embodiments,other factors may represent the second digital data element.

At step 203, the secure intelligent agent determines a third digitaldata element. In various exemplary embodiments, the third digital dataelement is an item on the determined segment. In further exemplaryembodiments, other factors may represent the third digital data element.

At optional step 204, steps 201 through 203 (i.e. intelligentdetermination of the first, second and/or third digital data elements)may be performed by a hardware based random number generator machinethat comprises part of the intelligent agent.

Optional step 204, according to many exemplary embodiments, improves thefunctioning of the exemplary system by optimizing the number of runsthat can be performed in a limited period of time. That is, in mostexemplary embodiments, the information generated has a time value. Astime lapses, the value of the information decreases. Additionally, giventhe massive amount of data to be processed in a limited period of time,it is important that the time be used as effectively as possible.Therefore employing random number generation to drive the variables forthe runs is far more effective than having a human drive the variablesfor the runs, as human involvement would likely lead to redundancy, biasand other inefficiencies, as often a desired goal is to maximize thenumber of runs that may be performed in a limited period of time.

At step 205, the intelligent agent accesses (via its circuitry over asecure network to a secure cloud of specialized intelligent historicalagents and/or virtual intelligent historical agents that may be loadbalanced) secure digital data for the first, second and third digitaldata elements.

At step 206, the intelligent agent determines a fourth digital dataelement. In some exemplary embodiments, the fourth digital data elementis a look back period. In further exemplary embodiments, other factorsmay represent the fourth digital data element. This step may also beperformed by the hardware based random number generator machine, eitheras a separate step or as part of optional step 204.

At step 207, the intelligent agent directs (via its circuitry over asecure network) the secure cloud of specialized intelligent historicalagents and/or virtual intelligent historical agents that may be loadbalanced to send secure digital data for the first, second, third andfourth digital data elements to a plurality of secure cloud basedspecialized insight servers and/or virtual insight machines. In someembodiments, security of such activity may be established through avirtual private network (“VPN”).

According to many exemplary embodiments, the secure cloud of specializedintelligent historical agents and/or virtual intelligent historicalagents function in parallel to divide the data to be transferred to theplurality of cloud based specialized insight servers and/or virtualinsight machines, copy the secure digital data, proportion the copiedsecure digital data among a series of secure channels and transmit thedata to the plurality of specialized insight servers and/or virtualinsight machines.

At step 208, the intelligent agent determines a fifth digital dataelement. According to various exemplary embodiments, a performancemetric is the fifth digital data element. In further exemplaryembodiments, other factors may represent the fifth digital data element.This step may also be performed by the hardware based random numbergenerator machine, either as a separate step or as part of optional step204.

At step 209, the intelligent agent determines a sixth digital dataelement. According to various exemplary embodiments, a condition is thesixth digital data element. In further exemplary embodiments, otherfactors may represent the sixth digital data element. This step may alsobe performed by the hardware based random number generator machine,either as a separate step or as part of optional step 204.

At step 210, the intelligent agent determines a seventh digital dataelement. According to various exemplary embodiments, an allocationamount is the seventh digital data element. In further exemplaryembodiments, other factors may represent the seventh digital dataelement.

At step 211, the intelligent agent directs (via its circuitry over asecure network) the secure cloud of specialized insight servers and/orvirtual insight machines to transform the secure digital data for thefirst, second, third and fourth digital data elements pursuant to thefifth, sixth and seventh digital data elements.

According to many exemplary embodiments, data transformation isoptimized by one way secure digital data delivery across expandablecomputing resources. This includes not transmitting back the securedigital data for the first through seventh digital data elements totheir origins. Instead, the fast destruction of this data is performedso as to speed up the transformation of a subsequent run.

At step 212, the intelligent agent determines an eighth digital dataelement. According to one exemplary embodiment, a percentagerepresenting a percentage of the transformed results that should beselected is the eighth digital data element. In further exemplaryembodiments, other factors may represent the eighth digital dataelement.

At optional step 213, the intelligent agent determines a ninth digitaldata element. According to one exemplary embodiment, an upper and/orlower outlier factor, representing a particular percentage of thehighest and/or lowest of the transformation results to remove beforeperforming step 212 is the ninth digital data element.

At step 214, the intelligent agent directs (via its circuitry over asecure network) the secure cloud of specialized insight servers and/orvirtual insight machines to further transform the transformation resultsgenerated at step 211 pursuant to step 212 and optionally, step 213.According to one exemplary embodiment, the further transformationresults in a list of names and/or identifiers of portions of thetransformed results, but not the portions themselves.

In many exemplary embodiments, the transformation results generated atstep 211 pursuant to step 212 and optionally, step 213 represent amassive amount of data. At step 214, computing performance is improvedby scrubbing or clearing the data transferred from the secure cloud ofspecialized intelligent historical agents and/or virtual intelligenthistorical agents from names and/or identifiers of parts of that data.The names and/or identifiers are based on the output or outcome of thetransformation performed at step 211 pursuant to step 212 andoptionally, step 213. The amount of data represented by the names and/oridentifiers is extremely small when compared to the secure digital datatransferred from the secure cloud of specialized historical agentsand/or virtual historical intelligent agents. Because the datatransferred from the secure cloud of specialized intelligent historicalagents and/or virtual intelligent historical agents was copied beforethe transmission, this data still resides in the secure cloud ofspecialized intelligent historical agents and/or virtual intelligenthistorical agents. The scrubbed names and/or identifiers is transformedinto a list that may also include specification of digital data elementsone through eight. The list is then securely transmitted to anintelligent operational agent.

The functioning of the intelligent operational agent, according tovarious exemplary embodiments, is enhanced by being focused on matchingstrategies to current conditions by being focused on such and not beinghindered by having to consume time and resources in managing andprocessing the corresponding secure digital data that resides in thesecure cloud of specialized intelligent historical agents and/or virtualintelligent historical agents. In comparison to systems to where bothdata sets reside in the same machine, the intelligent operational agentcan more quickly match a strategy to a current condition and make adeployment.

According to further exemplary embodiments, a scrubbed situationaldeployment trigger such as exemplary scrubbed situational deploymenttrigger 107 (FIG. 1 ) is the transformed product produced at step 214.As described herein, most if not all of the secure digital data employedto generate the scrubbed situational deployment trigger 107 has beenscrubbed by the clearing of the secure digital data from the securecloud based specialized insight servers and/or the virtual insightmachines 105 (FIG. 1 ). The scrubbed situational deployment trigger 107comprises the names/identifiers of strategies determined at step 214 sothat the strategies may be quickly recalled from the secure cloud basedspecialized intelligent historical agent 101 (FIG. 1 ) upon theoccurrence of a particular current condition. In many exemplaryembodiments, the scrubbed situational deployment trigger 107 alsocomprises digital data elements one through nine. After it is generated,the scrubbed situational deployment trigger 107 is transmitted from thesecure cloud based specialized insight servers and/or the virtualinsight machines 105 to the secure intelligent operational agent 104. Asdescribed in connection with step 215, upon the occurrence of aparticular current condition (e.g. the tenth digital data element) thatis the same as or approximates the condition for which the scrubbedsituational deployment trigger 107 was produced (e.g. the six digitaldata element), the scrubbed situational deployment trigger 107 willcause the transmission of the named/identified strategies from thesecure cloud based specialized intelligent historical agent 101 forexecution at the activity server 102 (FIG. 1 ).

At step 215 the intelligent operational agent determines a tenth digitaldata element and executes a test deployment strategy.

According to various exemplary embodiments, the tenth digital dataelement is a current condition. In some embodiments, the currentcondition may be similar to the sixth digital data element, a condition.Accordingly, the intelligent operational agent may access the list ofnames and/or identifiers corresponding to the sixth digital dataelement. Upon receiving a request from the intelligent operationalagent, the secure cloud of specialized intelligent historical agentsand/or virtual historical intelligent agents will function in parallelto send the secure digital data to the intelligent operational agent.The intelligent operational agent will then test deploy the transferreddata in a setting such as that exemplified by the activity server 102(FIG. 1 ).

According to further exemplary embodiments, the intelligent operationalagent may transmit to the activity server one or more decoy strings inthe same string to confuse any unwanted hackers attempting to interceptsuch information. The activity server would only actually deploy theactual strategy.

At step 216, if the test deployment strategy performed at step 215 wassuccessful, the intelligent operational agent will actually execute thedeployment strategy.

FIG. 3 is a table of exemplary digital data elements for secureintelligent networked architecture, processing and execution.

Shown in table 300 of FIG. 3 are ten exemplary digital data elements,including the corresponding name of each of the digital data elements.The first digital data element is a resource. Resources may includefootball players, workers, equities, vehicles, biological based drugsand the like. The second digital data element is a resource segment.Resource segments may include college football, lawyers, New York StockExchange (“NYSE”), trucks, monoclonal antibodies (“Mabs”) and such. Thethird digital data element is an item. Items may include running backs,patent lawyers, GM stock, Ford trucks, cancer Mabs, etc. The fourthdigital data element is a look back period. Look back periods mayinclude without limitation the last two seasons, last month, last tenyears, last thirty years, last two years, or last 5000 patients, etc.The fifth digital data element is a performance metric. Performancemetrics may include, without limitation, yards per carry, hours billed,risk adjusted performance, miles per gallon, disease free months. Thesixth digital data element is a condition. Conditions may include,without limitation, snow, recession, bear market, record (high or low)gas prices, lung adenocarcinoma, or a drought. The seventh digital dataelement is an allocation amount. Allocation amounts may include, withoutlimitation, number of players, workers, dollars, vehicles, drugs,computing resources or contracts. The eighth digital data element is apercentage for selection. Percentages for selection may include, withoutlimitation, top 10%, top 2%, top 1%, top 4%, or top 30%. The ninthdigital data element is an outlier percentage to remove from a topand/or bottom of a data set before executing selection pursuant to theeighth digital data element. Outlier percentages may include, withoutlimitation, top 1% and bottom 1%, top 5% and bottom 1%, not applicableor doesn't apply (“N/A”), top 2% or bottom 10%. The tenth digital dataelement is a current condition. Current conditions may include, withoutlimitation, snow, recession, bear market, record (high or low) gasprices, lung adenocarcinoma, or a drought.

FIGS. 4A-4B represent one example of the application of intelligentnetworked architecture, processing and execution shown in exemplarymethod 400.

At step 401, a secure intelligent agent determines a first digital dataelement. In some exemplary embodiments, the first digital data elementmay include a resource. For example, resources may include metals,energies, currencies, softs, grains, meats, and interest rates.

Here, for example, equities may be selected as the first digital dataelement.

At step 402, the secure intelligent agent determines a second digitaldata element. In some exemplary embodiments, the second digital dataelement is a segment for the resource. For example, segments may includethe Chicago Mercantile Exchange (“CME”), New York Mercantile Exchange(“NYMEX”) and/or the Intercontinental Exchange (“ICE”).

Here, for example, the New York Stock Exchange (“NYSE”) may be selectedas the second digital data element.

At step 403, the secure intelligent agent determines a third digitaldata element. In various exemplary embodiments, the third digital dataelement is an item on the determined segment. For example, items mayinclude gold (“GC”), crude oil (“CL”), and/or the S&P 500 (“ES”).

Here, for example, General Motors (“GM”) stock may be selected as thethird digital data element.

At optional step 404, steps 401 through 403 (i.e. intelligentdetermination of the first, second and/or third digital data elements)may be performed by a hardware based random number generator machinethat comprises part of the intelligent agent.

At step 405, the intelligent agent accesses (via its circuitry over asecure network to a secure cloud of specialized intelligent historicalagents and/or virtual intelligent historical agents that may be loadbalanced) secure digital data for the first, second and third digitaldata elements. For example, the secure digital data for the first,second and third digital data elements may be trading strategies,including algorithms or bots.

According to some exemplary embodiments, data processing may be managedusing simultaneous requests without the need to have multiple copies ofprograms executing within any single intelligent agent (e.g.“multithreading”).

Here, for example, an entire population (or optionally a segment of theentire population) of algorithms or bots that trade GM stock on the NYSEmarket is the secure digital data. For example, data for 160,000 bots(or more) are selected. These bots may be generated per U.S. NonProvisional application Ser. No. 14/642,569 filed on Mar. 9, 2015 titled“Systems and Methods for Generating and Selecting Trading Algorithms forBig Data Trading in Financial Markets,” which is hereby incorporated byreference, or the bots may be generated by other systems and methods.

According to certain exemplary embodiments, a trading algorithm, atrading strategy or a bot comprises a technical indicator, an evaluationbar characteristic and an item.

A technical indicator at the most basic level is a series of data pointsthat are derived by applying a formula to price data of an item.Technical indicators provide a unique perspective on the strength anddirection of the underlying price action of the item. Exemplarytechnical indicators include, but not by way of limitation, relativestrength index (“RSI”), average directional index, stochastics, moneyflow index, moving average convergence-divergence, etc.

A bar is comprised of an opening price, a closing price, interveningprices, volume and trading activity across a period of time for an item.For example, the price of gold may open at $800 per ounce on an exchangeat 9:00 AM and close at $900 per ounce on the same exchange at 5:00 PMon the same day. This may represent one bar.

Evaluation bar characteristics may be based on time, tick, volume, ormarket activity. For example, time (e.g., second, minute, hour, day,month etc.), and/or tick (trades at the exchange, e.g., x number oftrades) and/or volume (e.g., one, ten, two-hundred, one-thousand etc.contracts), and/or market-activity (e.g., 0.5%, 1%, 1.5%, 2% etc. marketmove).

Items may include futures (e.g., S&P, Euro, gold, crude, cotton,soybeans, 10-yr notes, lean hogs, etc.), stocks (e.g., PG, GE, AAPL,GOOG, FB, etc.), bonds (e.g., US Gov. bonds, Eurodollar, etc.), andforex (e.g., EURUSD euro to the dollar, etc.).

Here, for example, the relative strength index (“RSI”) may be selectedas a technical indicator. Every thirty seconds may be selected as anevaluation bar characteristic. The price of gold may be selected as thetradable item. Accordingly, a bot may comprise deciding whether to buy,sell or hold based on calculating the RSI of the price of gold every 31seconds while the relevant market is open.

A secure cloud of specialized historical intelligent historical agentsand/or virtual intelligent historical agents, according to someexemplary embodiments, may comprise trading algorithms, tradingstrategies or “bots” that meet minimum standards.

In some embodiments, minimum standards refer to anything that is tradeworthy. Minimum standards vary for different preliminary tests. Forexample, if the strategy is to look for safe trading algorithms,filtering criteria focus on safety (minimal losses) in unfavorablemarket conditions such as volatile or bearish market periods. If thestrategy is to look for high performing trading algorithms, filteringcriteria focus on superior returns such as any trading algorithm with ahigh annual return (i.e., greater than 50% return).

At step 406, the intelligent agent determines a fourth digital dataelement. In some exemplary embodiments, the fourth digital data elementis a look back period. In further exemplary embodiments, other factorsmay represent the fourth digital data element. This step may also beperformed by the hardware based random number generation machine, eitheras a separate step or as part of optional step 404. Here, for example,the intelligent agent may determine a look back period of 10 years asthe fourth digital data element.

At step 407, the intelligent agent directs (via its circuitry over asecure network) the secure cloud of specialized intelligent historicalagents and/or virtual intelligent historical agents that may be loadbalanced to send secure digital data for the first, second, third andfourth digital data elements to a plurality of secure cloud basedspecialized insight servers and/or virtual insight machines. In someembodiments, security of such activity may be established through avirtual private network (“VPN”).

Here, for example, the past 10 years of transaction data (on atransaction by transaction basis) for each of the bots that trade GM aswell as the past 10 years of market price data (on a minute by minutebasis) is the secure digital data for the first, second, third andfourth digital data elements that the intelligent agent determines anddirects the transmission between cloud resources.

Here, for example, transaction information for the 160,000 bots andcopies of the bots are transmitted. Load balancing of the servers mayalso be performed as directed by the intelligent agent or anotherspecialized machine.

At step 408, the intelligent agent determines a fifth digital dataelement. According to various exemplary embodiments, a performancemetric is the fifth digital data element. This step may also beperformed by the hardware based random number generation machine, eitheras a separate step or as part of optional step 404. Here, for example,risk adjusted performance is determined by the intelligent agent as thefifth digital data element.

According to some exemplary embodiments, bots or trading algorithms maybe grouped together in each market by each performance metric. A groupof bots filtered by average drawdown is focused on risk/safety. A groupof bots filtered by MAR is focused on risk-adjusted returns since risknow taken into account (unlike CAGR).

According to other exemplary embodiments, multiple performance metricsmay be selected at step 408. Such performance metrics may include,however, not limited to: Compounded Annual Growth Rate (CAGR), TimeWeighted Rate of Return (TWRR), Holding Period Return (HPR), Jenson'sAlpha, Underwater Volume Index (UVI), Abovewater Volume Index (AVI),Average Drawdown, Maximum Drawdown, MAR Ratio, Total Decision Points,Trades Per Million, Best 12 Months, Worst 12 Months, Return RetracementRatio, Sortino Ratio, Sharpe Ratio, Percent in the Market, Total Numberof Trades, Average Trade Length, Average Trades Per Day, Average TradesPer Week, Average Trades Per Month, Percent Return in the Last Week,Percent Return in the Last 4 Weeks, Number of trades in the Last Week,Number of trades in the Last 4 Weeks, Commission, Slippage, Time SpentLong in the Market, Time Spent Short in the Market, Average Profit/Lossper Day, Average Profit/Loss per Week, Average Profit/Loss per Month,K-Ratio, and/or RINA Index.

At step 409, the intelligent agent determines a sixth digital dataelement. According to various exemplary embodiments, a condition is thesixth digital data element. In further exemplary embodiments, otherfactors may represent the sixth digital data element. This step may alsobe performed by the hardware based random number generation machine,either as a separate step or as part of optional step 404. Here, forexample, a bear market condition may be selected as the sixth digitaldata element, so that the risk adjusted performance for each of the botstrading GM stock is calculated for the bear markets that took placeduring the last 10 years. According to other exemplary embodiments,market conditions may also include cyclical bull, cyclical bear,volatile and/or ranging.

At step 410, the intelligent agent determines a seventh digital dataelement. According to various exemplary embodiments, an allocationamount is the seventh digital data element. In further exemplaryembodiments, other factors may represent the seventh digital dataelement. Here, the allocation amount is a number of contracts to deploy.

According to other exemplary embodiments, the allocation amount may beto a single strategy, a group of strategies, and/or to multiple groupsof strategies. For example, the maximum number of contracts a groupingof strategies will trade may be set and the grouping of strategies willsystematically trade based on the strategies' signals. In anotherexample, a fixed percentage of total equity or total value may representan allocation amount. Accordingly, with a $1,000,000 total value, themaximum number contracts may be set so that the margin to equity ratiois 10%. The maximum number of contracts traded will change as the equitychanges and as the margin on a given contract adjusts. In anotherexample, risk based allocation may be used. Here, based on a riskmetric, contracts are allocated to the best risk-return ratio in realtime. In yet a further example, a machine learning algorithm allocationmay be used. In this case, based on the machine learning algorithm,contract allocation will be distributed to the highest expectancy score.

At step 411, the intelligent agent directs (via its circuitry over asecure network) the secure cloud of specialized insight servers and/orvirtual insight machines to transform the secure digital data for thefirst, second, third and fourth digital data elements pursuant to thefifth, sixth and seventh digital data elements. In one exemplaryembodiment, the transformation will result in visually perceptibleelements. In further exemplary embodiments, other factors may representthe transformation result.

Here, for example, visually perceptible elements may representperformance of the 160,000 bots based upon the determined performancemetric and market condition. For example, based upon the pasttransaction history for each bot on a minute by minute basis, a visuallyperceptible element representing the risk adjusted performance for eachof the bots trading GM stock is calculated for the bear markets thattook place during the last 10 years.

Here, for example, if a bot made 20 transactions over the last 10 years,these transactions can be evaluated in order to calculate a performancemetric. Other information about the bot may be determined based onobserving the bot's minute by minute performance against its transactionhistory, including trade by trade metrics, compound annual growth rate(CAGR), underwater volume index (UVI), drawdown evaluation metrics, etc.

Performance metrics may include total profit over 1 year, percentage ofprofitable trades over a time period, how much was gained or lost ineach trade, percentage of profitable trades in a bear or bull market,correlation to other indexes, ratio of profitable trades to coverlargest loss, etc. For instance, % of profitable trades (=profitabletrades/total number of trades) may be used as performance metrics.

According to other exemplary embodiments, performance metrics may alsoinclude maximum drawdown, average drawdown, max peak to trough time,average peak to trough time, max peak to peak time, average peak to peaktime, pain to gain ration, sortino ratio, sharpe ratio, and/or returnretracement ratio.

At step 412, the intelligent agent determines an eighth digital dataelement. According to one exemplary embodiment, a percentagerepresenting a percentage of the transformed results that should beselected is the eighth digital data element. According to one exemplaryembodiment, a percentage representing what percent of the bot populationshould be selected for active trading based upon step 411 is the eighthdigital data element. Here, for example, for the plurality ofspecialized servers each having an insight engine, 1% is determined,meaning that the top 1% of the 160,000 bots should be selected based therisk adjusted return for trading GM stock (regardless of frequency) forthe bear markets during past 10 years, resulting in a strategic tradingportfolio of 1600 selected bots. According to another exemplaryembodiment, a predetermined minimum threshold may be set as the eighthdigital element. In this case, bots meeting and/or exceeding such athreshold are selected.

At optional step 413, the intelligent agent determines a ninth digitaldata element. According to one exemplary embodiment, an upper and/orlower outlier factor, representing a particular percentage of thehighest and/or lowest of the transformation results to remove beforeperforming step 412 is the ninth digital data element. According to oneexemplary embodiment, an upper and/or lower outlier factor, representinga particular percentage of the highest and/or lowest performing bots toremove before performing step 412 is the eighth digital data element.For example, the intelligent agent may employ multiple statisticalregression methods to systematically determine which bots performoutside of a mean of a particular population. Such methods may include,but are not limited to grouping methods (e.g. ignoring the top 5% basedon the selected performance metric and grouping the first 100 and 300 ofthe remaining bots), elimination methods (standardization ornormalization of populations through elimination of the top and bottom5% of bots outside of a statistical mean), using statistical measuresDFITTS and/or DFBETAS to identify outliers, or mathematical calculationssuch as standard error and standard deviation. In further exemplaryembodiments, other factors may represent the eighth digital dataelement.

At step 414, the intelligent agent directs (via its circuitry over asecure network) the secure cloud of specialized insight servers and/orvirtual insight machines to further transform the transformation resultsgenerated at step 411 pursuant to step 412 and optionally, step 413.According to one exemplary embodiment, the further transformationresults in a list of names/identifiers of the bots in each strategictrading portfolio (and not the bots themselves). The list may includethe variables defined by the intelligent agent during the run as well asthe resulting performance metric(s) for each bot and for the strategictrading portfolio. The list may also include a market condition for thestrategic trading portfolio. The list may be transmitted to anintelligent operational agent for further analysis.

At step 415 the intelligent operational agent determines a tenth digitaldata element and executes a test deployment strategy.

According to various exemplary embodiments, the tenth digital dataelement is a current condition. In some embodiments, the currentcondition may be similar to the sixth digital data element, a condition.Accordingly, the intelligent operational agent may access the list ofnames and/or identifiers corresponding to the sixth digital dataelement. Upon receiving a request from the intelligent operationalagent, the secure cloud of specialized intelligent historical agentsand/or virtual historical intelligent agents will function in parallelto send the secure digital data to the intelligent operational agent.The intelligent operational agent will then test deploy the transferreddata in a setting such as that exemplified by the activity server 102(FIG. 1 ).

According to further exemplary embodiments, the intelligent operationalagent may transmit to the activity server one or more decoy strings inthe same string to confuse any unwanted hackers attempting to interceptsuch information. The activity server would only actually deploy theactual strategy.

At step 416, if the test deployment strategy performed at step 415 wassuccessful, the intelligent operational agent will actually execute thedeployment strategy.

The exemplary systems and methods described herein may be performed in asecure computing environment including the use of firewalls andencryption technology. Given the potentially high value of theinformation being generated, and the potential magnitude of theresulting investment decisions, measures may be taken to perform some orall of the steps herein in a secure manner, with emphasis on such stepsas the determination of strategy and execution of trades. For example,in addition to an optimal strategy, non-optimal strategies may purposelybe added in the same string or digital data environment of the optimalstrategy to confuse any unwanted hackers intercepting such information.As another example, in addition to a desired trade to be executed,undesired trades may purposely be added in the same string or digitaldata environment of the desired trade to confuse any unwanted hackersintercepting such information. Further, the desired trade may receivefunding for execution, whereas the undesired trades may not receivefunding for execution.

FIGS. 5A-5B represent another example of the application of intelligentnetworked architecture, processing and execution shown in exemplarymethod 500.

At step 501, a secure intelligent agent determines a first digital dataelement. In some exemplary embodiments, the first digital data elementmay include a resource.

Here, the resource is a large collection of biological drugs.

At step 502, the secure intelligent agent determines a second digitaldata element. In some exemplary embodiments, the second digital dataelement is a segment for the resource.

Here, the segment for the resource are monoclonal antibody (“Mab”) basedbiological drugs.

At step 503, the secure intelligent agent determines a third digitaldata element. In various exemplary embodiments, the third digital dataelement is an item on the segment.

Here, the item on the segment are monoclonal antibody (“Mab”) baseddrugs for treating cancer.

At optional step 504, steps 501 through 503 (i.e. intelligentdetermination of the first, second and/or third digital data elements)may be performed by a hardware based random number generator machinethat comprises part of the intelligent agent.

Optional step 504, according to many exemplary embodiments, improves thefunctioning of the exemplary system by optimizing the number of runsthat can be performed in a limited period of time. That is, in mostexemplary embodiments, the information generated has a time value. Astime lapses, the value of the information decreases. Additionally, giventhe massive amount of data to be processed in a limited period of time,it is important that the time be used as effectively as possible.Therefore employing random number generation to drive the variables forthe runs is far more effective than having a human drive the variablesfor the runs, as human involvement would likely lead to redundancy, biasand other inefficiencies, as often a desired goal is to maximize thenumber of runs that may be performed in a limited period of time.

At step 505, the intelligent agent accesses (via its circuitry over asecure network to a secure cloud of specialized intelligent historicalagents and/or virtual intelligent historical agents) secure digital datafor the first, second and third digital data elements.

At step 506, the intelligent agent determines a fourth digital dataelement. In some exemplary embodiments, the fourth digital data elementis a look back period. This step may also be performed by a hardwarebased random number generation machine, either as a separate step or aspart of optional step 504.

Here, the look back period is represented by the last 50,000 patientsseen or treated with monoclonal antibody (“Mab”) based drugs for cancer.

At step 507, the intelligent agent directs (via its circuitry over asecure network) the secure cloud of specialized intelligent historicalagents and/or virtual intelligent historical agents to send securedigital data for the first, second, third and fourth digital dataelements to a plurality of secure cloud based specialized insightservers and/or virtual insight machines. In some embodiments, securityof such activity may be established through a virtual private network(“VPN”).

According to many exemplary embodiments, the secure cloud of specializedintelligent historical agents and/or virtual intelligent historicalagents function in parallel to divide the data to be transferred to theplurality of cloud based specialized insight servers and/or virtualinsight machines, copy the secure digital data, proportion the copiedsecure digital data among a series of secure channels and transmit thedata to the plurality of specialized insight servers and/or virtualinsight machines.

Here, the secure digital data includes the medical histories and genomicdata for the last 50,000 patients seen or treated with monoclonalantibody (“Mab”) based drugs for cancer.

At step 508, the intelligent agent determines a fifth digital dataelement. According to various exemplary embodiments, a performancemetric is the fifth digital data element. In further exemplaryembodiments, other factors may represent the fifth digital data element.This step may also be performed by the hardware based random numbergeneration machine, either as a separate step or as part of optionalstep 504.

Here, the number of disease free months is the performance metric.

At step 509, the intelligent agent determines a sixth digital dataelement. According to various exemplary embodiments, a condition is thesixth digital data element. This step may also be performed by thehardware based random number generation machine, either as a separatestep or as part of optional step 504.

Here, the condition is adenocarcinoma of the lung, with a particulargene sequence.

At step 510, the intelligent agent determines a seventh digital dataelement. According to various exemplary embodiments, an allocationamount is the seventh digital data element. In further exemplaryembodiments, other factors may represent the seventh digital dataelement.

Here, the allocation amount is the amount of computing resources thatmay be used in performing step 511.

At step 511, the intelligent agent directs (via its circuitry over asecure network) the secure cloud of specialized insight servers and/orvirtual insight machines to transform the secure digital data for thefirst, second, third and fourth digital data elements pursuant to thefifth, sixth and seventh digital data elements.

According to many exemplary embodiments, data transformation isoptimized by one way secure digital data delivery across expandablecomputing resources. This includes not transmitting back the securedigital data for the first through sixth digital data elements to theirorigins. Instead, the fast destruction of this data is performed so asto speed up the transformation of a subsequent run.

According to exemplary embodiments, the secure cloud of specializedinsight servers and/or virtual insight machines analyze the securedigital data for the last 50,000 patients treated with monoclonalantibody based biological drugs for adenocarcinoma of the lung, with aparticular gene sequence to determine the drug(s) that maximized diseasefree months.

Additionally, the genomic data of this patient population will beanalyzed for commonalities and differences, especially as it relates tothe specific treatment(s) received. This includes analyzing genesequences from lung tissue biopsies, taking into account cell types.Additionally, determinations may be made about “cocktail” treatmentswhere based on the analysis, a plurality of monoclonal antibody basedbiological drugs would appear to have additive impact with respect tothe particular performance metric selected. Such cocktail treatments mayalso include suggested treatments based on information obtained byanalyzing the medical histories, such as dietary and exercise patterns.

At step 512, the intelligent agent determines an eighth digital dataelement. According to one exemplary embodiment, a percentagerepresenting a percentage of the transformed results that should beselected is the eighth digital data element. In further exemplaryembodiments, other factors may represent the eighth digital dataelement.

At optional step 513, the intelligent agent determines a ninth digitaldata element. According to one exemplary embodiment, an upper and/orlower outlier factor, representing a particular percentage of thehighest and/or lowest of the transformation results to remove beforeperforming step 512 is the ninth digital data element.

At step 514, the intelligent agent directs (via its circuitry over asecure network) the secure cloud of specialized insight servers and/orvirtual insight machines to further transform the transformation resultsgenerated at step 511 pursuant to step 512 and optionally, step 513.According to one exemplary embodiment, the further transformationresults in a list of names and/or identifiers of portions of thetransformed results, but not the portions themselves.

In many exemplary embodiments, the transformation results generated atstep 511 pursuant to step 512 and optionally, step 513 represent amassive amount of data. At step 514, computing performance is improvedby scrubbing or clearing the data transferred from the secure cloud ofspecialized intelligent historical agents and/or virtual intelligenthistorical agents from names and/or identifiers of parts of that data.The names and/or identifiers are based on the output or outcome of thetransformation performed at step 511 pursuant to step 512 andoptionally, step 513. The amount of data represented by the names and/oridentifiers is extremely small when compared to the secure digital datatransferred from the secure cloud of specialized historical agentsand/or virtual historical intelligent agents. Because the datatransferred from the secure cloud of specialized intelligent historicalagents and/or virtual intelligent historical agents was copied beforethe transmission, this data still resides in the secure cloud ofspecialized intelligent historical agents and/or virtual intelligenthistorical agents. The scrubbed names and/or identifiers is transformedinto a list that may also include specification of digital data elementsone through eight. The list is then securely transmitted to anintelligent operational agent.

The functioning of the intelligent operational agent, according tovarious exemplary embodiments, is enhanced by being focused on matchingstrategies to current conditions by being focused on such and not beinghindered by having to consume time and resources in managing andprocessing the corresponding secure digital data that resides in thesecure cloud of specialized intelligent historical agents and/or virtualintelligent historical agents. In comparison to systems to where bothdata sets reside in the same machine, the intelligent operational agentcan more quickly match a strategy to a current condition and make adeployment.

According to further exemplary embodiments, a scrubbed situationaldeployment trigger such as exemplary scrubbed situational deploymenttrigger 107 (FIG. 1 ) is the transformed product produced at step 514.As described herein, most if not all of the secure digital data employedto generate the scrubbed situational deployment trigger 107 has beenscrubbed by the clearing of the secure digital data from the securecloud based specialized insight servers and/or the virtual insightmachines 105 (FIG. 1 ). The scrubbed situational deployment trigger 107comprises the names/identifiers of strategies determined at step 514 sothat the strategies may be quickly recalled from the secure cloud basedspecialized intelligent historical agent 101 (FIG. 1 ) upon theoccurrence of a particular current condition. In many exemplaryembodiments, the scrubbed situational deployment trigger 107 alsocomprises digital data elements one through nine. After it is generated,the scrubbed situational deployment trigger 107 is transmitted from thesecure cloud based specialized insight servers and/or the virtualinsight machines 105 to the secure intelligent operational agent 104. Asdescribed in connection with step 515, upon the occurrence of aparticular current condition (e.g. the tenth digital data element) thatis the same as or approximates the condition for which the scrubbedsituational deployment trigger 107 was produced (e.g. the six digitaldata element), the scrubbed situational deployment trigger 107 willcause the transmission of the named/identified strategies from thesecure cloud based specialized intelligent historical agent 101 forexecution at the activity server 102 (FIG. 1 ).

According to one exemplary embodiment, the further transformationproduces a list of names and/or identifiers of optimal treatmentstrategies.

The scrubbed situational deployment trigger 107 comprises thenames/identifiers of strategies determined at step 514 (e.g. monoclonalantibody (“Mab” based drugs) so that the associated information may bequickly recalled from the secure cloud based specialized intelligenthistorical agent 101 (FIG. 1 ) upon the occurrence of a particularcurrent condition.

At step 515 the intelligent operational agent determines a tenth digitaldata element and executes a deployment strategy.

According to various exemplary embodiments, the tenth digital dataelement is a current condition. In some embodiments, the currentcondition may be similar to the sixth digital data element, a condition.Accordingly, the intelligent operational agent may access the list ofnames and/or identifiers corresponding to the sixth digital dataelement. Upon receiving a request from the intelligent operationalagent, the secure cloud of specialized intelligent historical agentsand/or virtual historical intelligent agents will function in parallelto send the secure digital data to the intelligent operational agent.The intelligent operational agent will then deploy the transferred datain a setting such as that exemplified by the activity server 102 (FIG. 1).

According to further exemplary embodiments, the intelligent operationalagent may transmit to the activity server one or more decoy strings inthe same string to confuse any unwanted hackers attempting to interceptsuch information. The activity server would only actually deploy theactual strategy.

Here, for example, the activity server may be connected to a genesequencer at a cancer treatment center. The sequencer may be sequencingthe DNA of a lung cancer biopsy tissue specimen. The scrubbedsituational deployment trigger 107 at the secure intelligent operationalagent 104 will be deployed based upon the occurrence of a particularcurrent condition (e.g. the tenth digital data element beingadenocarcinoma of the lung, with a particular gene sequence) that is thesame as or approximates the condition for which the scrubbed situationaldeployment trigger 107 was produced (e.g. the six digital data elementbeing adenocarcinoma of the lung, with a particular gene sequence), thescrubbed situational deployment trigger 107 causing the transmission ofthe associated information (e.g. genomic data and medical histories)from the secure cloud based specialized intelligent historical agent 101for execution at the activity server 102 (FIG. 1 ) in the form ofgenerating a personalized treatment plan.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. The descriptions are not intended to limit the scope of thetechnology to the particular forms set forth herein. Thus, the breadthand scope of a preferred embodiment should not be limited by any of theabove-described exemplary embodiments. It should be understood that theabove description is illustrative and not restrictive. To the contrary,the present descriptions are intended to cover such alternatives,modifications, and equivalents as may be included within the spirit andscope of the technology as defined by the appended claims and otherwiseappreciated by one of ordinary skill in the art. The scope of thetechnology should, therefore, be determined not with reference to theabove description, but instead should be determined with reference tothe appended claims along with their full scope of equivalents.

What is claimed:
 1. An intelligent networked architecture comprising: anintelligent agent having a hardware processor, the intelligent agentconfigured to utilize a random number generator machine to optimizevariables for processing runs; a specialized intelligent historicalagent having a hardware processor and a memory further comprising securedigital data; a specialized insight server having a hardware processor,the specialized insight server configured to receive from thespecialized intelligent historical agent the secure digital data andconfigured to transform the secure digital data into a scrubbedsituational deployment trigger, the scrubbed situational deploymenttrigger being a reduced size version of the secure digital data; and anintelligent operational agent having a hardware processor, theintelligent operational agent configured to receive the scrubbedsituational deployment trigger and cause the specialized intelligenthistorical agent to transmit a complete version of the secure digitaldata to the specialized insight server.